Apparatus for and method of measuring blood pressure

ABSTRACT

An apparatus for and a method of measuring blood pressure are provided. The apparatus includes a sensor configured to radiate light to a body part, and detect a light signal that is changed due to the body part. The apparatus further includes a signal processor configured to determine a bio signal based on the light signal; and a central processing unit configured to determine a blood pressure based on the bio signal and a blood pressure estimation algorithm.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority from Korean Patent Application No.10-2015-0031967, filed on Mar. 6, 2015, in the Korean IntellectualProperty Office, the disclosure of which is incorporated herein byreference in its entirety.

BACKGROUND

1. Field

Apparatuses and methods consistent with exemplary embodiments relate toapparatuses for and methods of measuring blood pressure.

2. Description of the Related Art

Blood pressure is used as a measure of health. Sphygmomanometers aredevices for measuring blood pressure and are commonly used in medicalinstitutions and at home. In the case of a cuff-type sphygmomanometer, asystolic blood pressure and a diastolic blood pressure are measured byplacing a cuff around a body part through which arterial blood flows,inflating the cuff until the artery is occluded, and then slowlyreleasing the pressure in the cuff. However, the cuff-typesphygmomanometer causes inconvenience to a user due to the appliedpressure, and is inconvenient to carry to continuously monitor a changein the blood pressure of a person in real time for a long time.Accordingly, much research on cuffless sphygmomanometers for measuringblood pressure has been made.

SUMMARY

Exemplary embodiments address at least the above problems and/ordisadvantages and other disadvantages not described above. Also, theexemplary embodiments are not required to overcome the disadvantagesdescribed above, and may not overcome any of the problems describedabove.

Exemplary embodiments provide apparatuses for and methods of measuringblood pressure based on a light signal via a cuffless structure. Theblood pressure may be continuously monitored for a long time.

According to an aspect of an exemplary embodiment, there is provided anapparatus configured to measure blood pressure, the apparatus includinga sensor configured to radiate light to a body part, and detect a lightsignal that is changed due to the body part. The apparatus furtherincludes a signal processor configured to determine a bio signal basedon the light signal, and a central processing unit configured todetermine a blood pressure based on the bio signal and a blood pressureestimation algorithm.

The signal processor may be further configured to extract a cycle of thelight signal, and sample data from the cycle of the light signal atequidistant time intervals or based on a user input.

The signal processor may be further configured to compare powerspectrums within a frequency range of bio signals that are determinedbased on channels, and select a channel having a maximum power spectrumfrom the channels.

The signal processor may be further configured to, in response to thesignal processor selecting the channel having the maximum power spectrumor using a single channel, select a part of a bio signal thatcorresponds to the selected channel or the single channel, in which apower spectrum value within the frequency range is greater than a value,as a valid part of the bio signal.

The apparatus may further include a display configured to display theblood pressure.

The apparatus may further include a memory configured to store the bloodpressure estimation algorithm.

The sensor may include a light emitter configured to radiate the lightto the body part, and a light receiver configured to detect the lightsignal that is changed due to the body part. The light receiver mayinclude a photodiode or an image sensor, and the light emitter mayinclude a laser diode or a light emitting diode.

The sensor may include a light emitter configured to radiate the lightto the body part, and a light receiver configured to detect the lightsignal that is changed due to the body part, the light emitter mayinclude a laser diode, and the central processing unit may be furtherconfigured to determine the blood pressure based on the bio signal inresponse to the sensor being spaced apart from a skin of an examinee.

The bio signal may be periodically obtained at predetermined timeintervals.

The central processing unit may be further configured to determine theblood pressure based on the bio signal and one among a linear regressionanalysis algorithm, a multiple regression analysis algorithm, and anon-linear regression analysis algorithm.

The central processing unit may be further configured to determine theblood pressure based on the bio signal and one among an artificialneural network algorithm, a k-nearest neighbor algorithm, a Bayesiannetwork algorithm, a support vector machine algorithm, and a recurrentneural network algorithm.

The central processing unit may be further configured to correct theblood pressure based on a blood pressure that is determined by anotherdevice.

The apparatus may further include a body information interfaceconfigured to receive body information of at least one among an age, agender, a weight, and a height of an examinee, and the centralprocessing unit may be further configured to determine the bloodpressure based on the bio signal and the body information.

The apparatus may be portable, and may be implemented in one among awrist watch, a mobile smart phone, a tablet computer, an earphone, aheadset, and glasses.

The apparatus may be implemented in a wrist watch, and the sensor may bepositioned on a back of a main body or a strap of the wrist watch.

According to an aspect of another exemplary embodiment, there isprovided a method of measuring blood pressure, the method includingradiating light to a body part, detecting a light signal that is changeddue to the body part, and determining a bio signal based on the lightsignal. The method further includes correcting the bio signal,extracting feature points from the corrected bio signal, and combining amatrix of a blood pressure estimation algorithm with the feature pointsto determine a blood pressure.

The extracting may include determining a maximum point of the correctedbio signal and a minimum point adjacent to the maximum point, andextracting the feature points from the corrected bio signal atequidistant time intervals or based on a user input.

The matrix of the blood pressure estimation algorithm may be determinedby learning the blood pressure estimation algorithm such that the bloodpressure that is determined by inputting the feature points in the bloodpressure estimation algorithm is closer to an actual blood pressure.

The blood pressure estimation algorithm may be one among an artificialneural network algorithm, a k-nearest neighbor algorithm, a Bayesiannetwork algorithm, a support vector machine algorithm, and a recurrentneural network algorithm.

The correcting may include correcting a baseline of a sequence of thebio signal, and removing high frequency noise from the correctedsequence.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and/or other aspects will be more apparent by describingexemplary embodiments with reference to the accompanying drawings inwhich:

FIG. 1 is a block diagram of a blood pressure measuring apparatusaccording to an exemplary embodiment;

FIGS. 2 through 5 are layouts of a light emitting device and a lightreceiving device of a sensor according to exemplary embodiments;

FIG. 6 is a flowchart of an operation of a blood pressure measuringapparatus according to an exemplary embodiment;

FIG. 7 is a diagram illustrating a process of estimating blood pressurevia a blood pressure estimation algorithm according to an exemplaryembodiment;

FIGS. 8 through 10 are perspective views of portable devices, eachincluding a blood pressure measuring apparatus according to exemplaryembodiments;

FIG. 11 is a graph of a waveform when a sensor of a light emittingdiode-photo diode (LED-PD) combination radiates light toward a fingerand detects a change in a blood stream as a signal change of lightaccording to an exemplary embodiment;

FIGS. 12 through 14 are graphs of waveforms when a sensor of an LED-PDcombination radiates light toward a radial artery, to above a wrist, andto a finger, respectively, and detects a change in a blood stream as asignal change of light according to exemplary embodiments;

FIG. 15A is a graph of a measured pulse waveform according to anexemplary embodiment, and FIG. 15B is a graph of parameters that may beextracted from one cycle of the waveform of FIG. 15A;

FIG. 16 is a diagram illustrating an artificial neural network (ANN)algorithm according to an exemplary embodiment;

FIG. 17 is a diagram illustrating a linear regression analysis algorithmaccording to an exemplary embodiment;

FIG. 18 is a diagram illustrating a data learning process in an ANNalgorithm used by a blood pressure measuring apparatus according to anexemplary embodiment;

FIG. 19 is a diagram illustrating a process of calculating bloodpressure values by using a stored hidden layer matrix after extractingfeature points via an ANN algorithm according to an exemplaryembodiment; and

FIGS. 20A through 20C are diagram illustrating a process of collectingand predicting data that may be used during the data learning process ofFIG. 18 according to exemplary embodiments.

DETAILED DESCRIPTION OF THE EXEMPLARY EMBODIMENTS

Exemplary embodiments are described in greater detail herein withreference to the accompanying drawings.

In the following description, like drawing reference numerals are usedfor like elements, even in different drawings. The matters defined inthe description, such as detailed construction and elements, areprovided to assist in a comprehensive understanding of the exemplaryembodiments. However, it is apparent that the exemplary embodiments canbe practiced without those specifically defined matters. Also,well-known functions or constructions are not described in detailbecause they would obscure the description with unnecessary detail.

It will be understood that although the terms “first,” “second,” etc.may be used herein to describe various elements, these elements shouldnot be limited by these terms. These elements are only used todistinguish one element from another.

In addition, the terms such as “unit,” “-er (-or),” and “module”described in the specification refer to an element for performing atleast one function or operation, and may be implemented in hardware,software, or the combination of hardware and software.

FIG. 1 is a block diagram of a blood pressure measuring apparatus 10according to an exemplary embodiment.

Referring to FIG. 1, the blood pressure measuring apparatus 10 includesa sensor 100, a signal processor 200 that obtains a bio signal from asignal detected from the sensor 100, a memory 400 that stores a bloodpressure estimation algorithm, and a central processing unit 300 thatcalculates blood pressure based on the obtained bio signal by using theblood pressure estimation algorithm. The blood pressure measuringapparatus 10 further includes a display 330 that displays the calculatedblood pressure, a body information interface 310 that inputs bodyinformation to increase the accuracy of calculating the blood pressure,and a data transmitter 350 that transmits information about thecalculated blood pressure to another device.

The sensor 100 radiates light towards an examined body part, and detectsa signal change in the light that is caused by the examined body part.The sensor 100 includes a light emitter 110 and a light receiver 150.The light emitter 110 may include at least one light emitting device,and the light receiver 150 may include at least one light receivingdevice.

The light emitting device may be a light emitting diode (LED) or a laserdiode (LD). The light receiving device may include a photodiode or animage sensor, for example, a CMOS image sensor (CIS). A phototransistor(PTr) may be used as the light receiving device. The light receivingdevice may be configured to sense a signal change according to a bloodstream change of light scattered or reflected from the examined body,i.e., skin of an examinee or a blood vessel.

FIGS. 2 through 5 are layouts of a light emitting device 111 and a lightreceiving device 151 of the sensor 100 according to exemplaryembodiments. A case where the light emitting device 111 and the lightreceiving device 151 are disposed on a same substrate 101 will now bedescribed with reference to FIGS. 2 through 5 as examples.

Referring to FIGS. 2 through 4, the light emitting device 111 isdisposed in a center of the sensor 100, and the light receiver 150includes a plurality of light emitting devices 151 surrounding the lightemitting device 111.

In another example, referring to FIG. 5, the sensor 100 has a structurein which an array of a plurality of light emitting devices 111 and anarray of the plurality of light receiving devices 151 are disposed inparallel. In this regard, the array of the plurality of light emittingdevices 111 is disposed in the center of the sensor 100, and the arrayof the plurality of light receiving devices 151 is disposed along atleast one side of the array of the plurality of light emitting devices111. FIG. 5 illustrates an example in which arrays of the plurality oflight receiving devices 151 are respectively disposed along two sides ofthe array of the plurality of light emitting devices 111.

As shown in FIGS. 2 through 5, when the light emitting device 111 isdisposed in the center of the sensor 100 and the light emitting devices151 are disposed around the light emitting device 111, a light receivingamount of light scattered or reflected from an examined body part, forexample, a skin surface of an examinee or a blood vessel, may increase.The light emitting device 111 and the plurality of light receivingdevices 151 that configure the sensor 100 are not limited to those shownin FIGS. 2 through 5, and may have various layouts. For example, atleast one of the light emitting devices 151 may be disposed in thecenter of the sensor 100, and one or more light emitting devices 111 maybe disposed around the at least one of the light emitting devices 151.

Meanwhile, when a laser diode is used as the light emitting device 111of the light emitter 110, due to the directional property of a laserbeam emitted by the laser diode, a signal may be measured although thesensor 100 is spaced apart from the skin surface of the examinee. Thus,when a laser diode is used as the light emitting device 111, and theblood pressure measuring apparatus 10 is implemented as awristwatch-type device, a device having a structure in which the sensor100 is placed on the back side of a main body and the signal is measuredfrom the wrist or the back of a hand, may be implemented. Thewristwatch-type device includes a main body and a strip being worn onthe wrist. An adherence of the strip to the wrist may be better than anadherence of the main body to the wrist. The main body of thewristwatch-type device may be spaced apart from a skin surface of thewrist. When a laser diode is provided as the light emitting device 111and the sensor 100 is placed on the back side of the main body, theblood pressure may be measured irrespective of a state of wearing thedevice, i.e., a contact state or a non-contact state with respect to thewrist.

In this regard, when an LED is used as the light emitting device 111,according to the spread characteristics of light emitted by the LED, anoperation of measuring the blood pressure may be performed by moretightly closing the sensor 100 to the skin of the examinee than in thecase when a laser diode is used as the light emitting device 111.However, when an LED is used as the light emitting device 111, the bloodpressure may be measured even when the sensor 100 and the skin surfaceof the examinee are spaced apart from each other within a range in whicha signal is detectable.

Referring again to FIG. 1, the signal processor 200 obtains a bio signalfrom the signal detected from the sensor 100, and removes a signalchange component of light due to external illumination or an externalenvironment. The signal processor 200 analyzes an intensity change ofthe signal detected from the sensor 100 on a time basis. The bio signalmay be obtained by analyzing a fluctuation of a light signalcorresponding to a capacity change of a blood vessel (for example, ablood vessel on a finger, an upper side of a wrist or a radial artery ona lower side of the wrist) of the examined body. In this regard, theobtained bio signal may be a photoplethysmogram (PPG) signal convertedbased on a correlation of the analyzed fluctuation of the light signaland the capacity change. A digital to analogue converter (DAC) or an ADCmay be applied as the signal processor 200.

The signal processor 200 may include a waveform extracting unit that,for example, extracts in real time one cycle of a waveform of a signalthat is input, and a data extracting unit that samples data atequivalent time intervals or by using a user-defined method from onecycle of the waveform. The signal processor 200 may further include awaveform selecting unit.

The signal processor 200 may be configured to obtain the bio signal fromeach of a plurality of channels. The signal processor 200 may comparepower spectrums within a previously set frequency range (for example,about 0.66 Hz˜about 3 Hz) with respect to a waveform of a bio signalobtained from each of the plurality of channels, and select a channelhaving a largest power spectrum. As another example, the signalprocessor 200 may be configured to use a single channel. According touse of the selected channel or the single channel, the waveformselecting unit may select a waveform part of the bio signal in which apower spectrum value within a predetermined frequency range is greaterthan a previously set value as a valid waveform part.

The memory 400 may store a blood pressure estimation algorithm. Thememory 400 may store a program for processing and controlling the signalprocessor 200 and the central processing unit 300, and may also storedata that is input/output. That is, the memory 400 may store measurementresults of the sensor 100 or a bio signal obtained by the signalprocessor 200 via signal processing. The memory 400 may be configured tostore a bio signal obtained in real time in a buffer memory, and may beconfigured to call the blood pressure estimation algorithm and calculatea blood pressure.

The memory 400 may include at last one type of storage medium among, forexample, a flash memory type, a hard disk type, a multimedia card microtype, card type memory (for example, SD or XD memory, etc.), RandomAccess Memory (RAM), Static Random Access Memory (SRAM), Read-OnlyMemory (ROM), Electrically Erasable Programmable Read-Only Memory(EEPROM), Programmable Read-Only Memory (PROM), magnetic memory, amagnetic disk, and an optical disk.

The central processing unit 300 controls an operation of the sensor 100,and calculates blood pressure from the measured signal by using theblood pressure estimation algorithm. That is, the central processingunit 300 calculates blood pressure from the bio signal obtained byprocessing the signal measured by the sensor 100 in the signal processor200 by using the blood pressure estimation algorithm. The centralprocessing unit 300 may control the memory 400, the display 330, thesignal processor 200, the body information interface 310, etc.

The central processing unit 300 may analyze various feature points ofthe bio single by analyzing a waveform characteristic of the bio signal,for example, a PPG pulse wave signal. The central processing unit 300may also estimate blood pressure values by combining data of theanalyzed feature points and a matrix of the blood pressure estimationalgorithm. In this regard, the blood pressure values estimated by thecentral processing unit 300 may include a systolic blood pressure (SBP),a diastolic blood pressure (DBP), a heart rate (HR), etc.

The blood pressure values calculated by the central processing unit 300may be displayed on the display 330. The display 330 may be configuredto display the SBP and the DBP, and may be configured to display the HR.

The body information interface 310 may be configured to input at leastone piece of body information among an age, a gender, a weight, and aheight of the examinee to increase accuracy of calculating the bloodpressure. The central processing unit 300 may operate to estimate theblood pressure for each of the body information input through the bodyinformation interface 310. In this regard, when the blood pressureestimation algorithm is configured to collect data of a randomlyextracted population, the blood pressure measuring apparatus 10 may beconfigured by omitting the body information interface 310.

The data transmitter 350 transmits a result analyzed by the centralprocessing unit 300 to an external different device. The blood pressurevalues calculated and estimated by the central processing unit 300 maybe output through the display 330. The data transmitter 350 may transmitthe blood pressure values and a heat rate value to an external devicesuch as a smart phone or a computer by using, for example, acommunication protocol such as Bluetooth. The data transmitter 350 maybe used to connect devices or connect a device to a clinic to allow theclinic to provide various services.

In this regard, the external device may be not only the smart phone orthe computer but also, for example, medical equipment that usesinformation of an analyzed blood pressure, a printer that prints aresultant, or a display apparatus that displays an analysis result. Inaddition, the external device may be various devices such as a tabletPC, a personal digital assistant (PDA), a laptop, PC, and another mobileor non-mobile computing apparatus.

The data transmitter 350 may be connected to the external device by wireor wirelessly. For example, the data transmitter 350 may be configuredto communicate with the external device by using various communicationmethods such as Bluetooth communication, Bluetooth Low Energy (BLE)communication, Near Field Communication, WLAN (WiFi) communication,Zigbee communication, infrared Data Association (IrDA) communication,WFD (Wi-Fi Direct) communication, ultra wideband (UWB) communication,Ant+ communication, etc.

Meanwhile, the blood pressure measuring apparatus 10 may further includea user interface. The user interface may be for an interface of the userand/or the external device, and may include an input unit and an outputunit. In this regard, although the user is an individual of which bloodpressure is to be measured, i.e., an examinee, the user may also be aperson, such as a medical expert, who may use the blood pressuremeasuring apparatus 10. Thus, the term “user” may have wider range thanthat of the term “examinee.” The user interface may be used to inputinformation for operating the blood pressure measuring apparatus 10, andoutput an analyzed result. The user interface may include, for example,a button, a connector, a keypad, a display, etc., and may furtherinclude an element such as a sound output unit or a vibration motor.

The blood pressure measuring apparatus 10 may be configured to be mobileas a wearable device, a mobile phone, for example, a mobile smart phone,or a tablet device. That is, the blood pressure measuring apparatus 10may be mounted in a wearable device, a mobile phone, for example, amobile smart phone, or a tablet device. The blood pressure measuringapparatus 10 may be configured as a device to be put on a finger tomeasure the blood pressure, for example, a device of a finger tongstype.

For example, the blood pressure measuring apparatus 10 may beimplemented in a device that may be worn on the examinee, i.e., in thewearable device. In this regard, the wearable device may be implementedin a wrist watch type, a bracelet type, and a wrist band type, and maybe additionally implemented in various types such as a ring type, aglasses type, an earphone type, a headset type, or a hair band type.Some elements of the blood pressure measuring apparatus 10, for example,the sensor 100 and the signal processor 200, may be implemented in atype that may be worn by the examinee.

The blood pressure measuring apparatus 10 may be used as a device forestimating the blood pressure of the examinee and measuring a heart rateof the examinee by being applied instead of a sensor of a wrist watchtype wearable device that measures the heart rate only, for example, byusing the back of the main body (corresponding to a watch in a wristwatch). The blood pressure measuring apparatus 10 may be used as thedevice for estimating the blood pressure of the examinee and measuringthe heart rate of the examinee by being applied to a smart phone thatuses a light emitting device and a CIS.

FIG. 6 is a flowchart of an operation of the blood pressure measuringapparatus 10 according to an exemplary embodiment.

Referring to FIG. 6, the light emitter 110 of the sensor 100 radiateslight to skin by contacting or approaching a body part of an examinee,for example, a finger, a wrist, etc. (operation S100). In this regard,the light receiver 150 receives light reflected or scattered from thebody part (operation S200), and the signal processor 200 measures awaveform by changing an intensity of the received light into a currentor voltage (operation S300).

The signal processor 200 or the central processing unit 300 determineswhether a signal of the measured waveform is present or the waveform issuitable (operation S400). If the waveform is a waveform including aheart rate, operation S500 is performed. If not, operation S200 ofreceiving the reflected or scattered light is again performed.

The central processing unit 300 estimates blood pressure values usingthe measured waveform and a blood pressure estimation algorithm(operation S500). The display 330 displays the estimated blood pressurevalues and the heart rate (operation S600). In the operation ofestimating the blood pressure values, a blood pressure value that ismeasured by another device may be input in the blood pressure estimationalgorithm. In this case, the blood pressure estimation algorithm maycorrect the blood pressure values by using the blood pressure value thatis measured by the other device.

The blood pressure measuring apparatus 10 according to the exemplaryembodiment described above may read a change in a blood stream in afinger or an upper or lower side (a radial artery) of a wrist, as anintensity change in light, may estimate maximum and minimum points ofthe blood pressure, i.e., a SBP and a DBP, by applying the bloodpressure estimation algorithm, and may also estimate an HR.

FIG. 7 is a diagram illustrating a process of estimating blood pressurevia a blood pressure estimation algorithm according to an exemplaryembodiment.

Referring to FIG. 7(a), a bio signal is obtained by radiating light toan examined body and detecting a signal change of light by the examinedbody. In this regard, a baseline with respect to the obtained bio signalis analyzed. The graph of FIG. 7(a) shows an example of measuring thebio signal for 4 seconds. The bio signal may be obtained a plurality ofnumber of times at a time distance, for example, a predetermined timedistance, for processing data. As shown in the graph of the bio signalof FIG. 7(a), the baseline may not be constant. FIG. 7(a) shows anexample of drawing a primary function in which the baseline increasesover time. The baseline may be in a secondary function, a tertiaryfunction, or a function having a plurality of inflection points. In thisregard, the baseline may be a line connecting, for example, intermediatepoints of a maximum point and a minimum point, of each cycle waveform ofthe bio signal.

When the baseline is drawn in the primary, secondary, or tertiaryfunction by analyzing the baseline described above, a bio signal may beobtained as shown in FIG. 7(b) by correcting the baseline.

Next, referring to FIG. 7(c), when a bio signal includes high frequencyHF noise, the high frequency HF noise may be removed by processing thebio signal using, for example, a smoothing function or filter. In thisregard, when the bio signal does not include the high frequency HFnoise, high frequency HF noise removing processing, such as processingusing the smoothing function or the filter, may be omitted.

Next, a maximum point Max and a minimum point Min of each cycle waveformof the bio signal from which the high frequency HF noise is removed areanalyzed as shown in FIG. 7(d), and feature points are extracted asshown in FIG. 7(e). The feature points of each cycle waveform may beextracted at equivalent or equidistant time intervals or may beextracted by determining a point from which the feature points are to beextracted by using a user-defined method.

When data of the extracted feature points is combined with a bloodpressure estimation algorithm, as shown in FIG. 7(f), a blood pressureestimation value calculation results may be obtained. The blood pressureestimation value calculation result of FIG. 7(f) is obtained by using abio signal obtained for 4 seconds for each calculation.

As shown in the blood pressure estimation value calculation result ofFIG. 7(f), a SBP and a DBP may be estimated and a HR estimation valuemay be obtained through the process described above.

The blood pressure measuring apparatus 10 according to the exemplaryembodiment may estimate a blood pressure, and measure a HR, by beingapplied to various existing devices that only measure the HR usinglight.

FIGS. 8 through 10 are perspective views of portable devices 500, 700,and 1000, each including the blood pressure measuring apparatus 10according to exemplary embodiments. FIG. 8 illustrates an example of thewrist watch type device 500 in which the sensor 100 is installed in theback of a main body MB, light is radiated to the back of an arm or ahand, a change in a blood stream is detected as a signal change oflight, and no sensor is installed in a strap ST. FIG. 9 illustrates anexample of the wrist watch type device 700 in which the sensor 100 isprovided in the strap ST, light is radiated to a radial artery, and achange in a blood stream flowing in the radial artery is detected as asignal change of light. FIG. 10 illustrates an example of the smartphone 1000 in which an image sensor 1100, i.e., a CIS, and a lightsource 1200 are provided on the back of the smart phone 1000, and areused to detect a change in a blood stream flowing in a finger as asignal change of light. The image sensor 1100 and the light source 1200provided on the back of the smart phone 1000 may be respectively used asthe light emitting device 111 of the light emitter 110 and the lightreceiving device 151 of the light receiver 150 of the blood pressuremeasuring apparatus 10.

FIG. 11 is a graph of a waveform when a sensor of a light emittingdiode-photo diode (LED-PD) combination radiates light to a finger anddetects a change in a blood stream as a signal change of light accordingto an exemplary embodiment. FIGS. 12 through 14 are graphs of waveformswhen a sensor of a laser diode-photo diode (LD-PD) combination radiateslight to a radial artery, to above a wrist, and to a finger,respectively, and detects a change in a blood stream as a signal changeof light according to exemplary embodiments.

As seen from a comparison of FIG. 11 and FIGS. 12 through 14, when notonly a LED but also a laser diode is used as the light emitting device111 of the light emitter 110, a signal indicating the change in theblood stream may be measured. When the laser diode is used, even thoughthe sensor 100 is placed above the wrist, the signal indicating thechange in the blood stream may also be measured. When the light emittingdevice 111 suitable for the light emitter 110 is selected, the signalindicating the change in the blood stream may be measured not only atthe finger or the radial artery but also above the wrist, and the signalmay be analyzed to estimate a value of a HR and calculate an estimationvalue of a blood pressure value. In addition, when the light emittingdevice 111 suitable for the light emitter 110 is selected, the signalindicating the change in the blood stream may be measured without alimitation to a position of a body part, and the signal may be analyzedto calculate estimation values of the blood pressure values and the HRvalue.

The blood pressure measuring apparatus 10 according to the exemplaryembodiment described above may use one of a linear regression analysis,a multiple regression analysis, a non-linear regression analysis as ablood pressure estimation algorithm to calculate the blood pressureestimation value by using a plurality of pieces of feature point dataextracted with respect to each cycle waveform of a bio signal. As theblood pressure estimation algorithm, one of machine learning algorithms,for example, an artificial neural network (ANN) algorithm, a K-nearestneighbor (KNN) algorithm, a Bayesian network algorithm, a support vectormachine (SVM) algorithm, and a recurrent neural network algorithm may beused. In this regard, the machine learning algorithm may performprediction based on an already determined attribute through trainingdata, for example, may predict a blood pressure by training a pulsewaveform.

For example, the ANN algorithm is used to perform calculation withalready learned data. The learned data may be stored in the memory 400in a hidden layer matrix, the stored hidden layer matrix and newlymeasured data may be combined during an actual measurement, and adesired blood pressure estimation value may be calculated.

Although the blood pressure measuring apparatus 10 may not use the bodyinformation interface 310 according to circumstances, a learned matrixmay be used as data collected from a population for each body featurerather than data collected from a randomly extracted population to moreaccurately calculate the blood pressure estimation value. Estimated datamay be output on the display 330. The data transmitter 350 may transmitthe blood pressure values and the HR value to a smart phone or acomputer by using a communication protocol such as Bluetooth. The datatransmitter 350 may be used to connect devices or connect a device to aclinic to allow the clinic to provide various services.

FIG. 15A is a graph of a measured pulse waveform according to anexemplary embodiment, and FIG. 15B is a graph of parameters that may beextracted from one cycle of the waveform of FIG. 15A.

Referring to FIGS. 15A and 15B, a waveform corresponding to one cycle ofa pulse waveform is extracted, and parameters, for example, t1, t2, t3,etc., that may be estimated to be related to a blood pressure areextracted. The waveform of FIGS. 15A and 15B may correspond to, forexample, a PPG pulse.

Referring to FIG. 15B, t1 denotes a systolic upstroke time, t2 denotes adiastolic time, and t3 denotes a width of a waveform in a predeterminedlocation. t3 data may be obtained in a plurality of locations. Data of awidth during a systolic time and a width during the diastolic time inthe predetermined location or in the plurality of locations may beobtained as the t3 data.

As described above, the parameters relating to the blood pressure may beextracted from the pulse waveform, and may be used to estimate a bloodpressure value.

FIG. 16 is a diagram illustrating an ANN algorithm according to anexemplary embodiment.

Referring to FIG. 16, a plurality of hidden layers, for example, twolayers, which are hidden between a maximum point and a minimum point ofblood pressure values corresponding to values of feature points of a biosignal obtained as described above, may be set in an input layer of theANN algorithm. The values of the feature points and blood pressurevalues may be used to form a network such as a neural network. In suchstructure, a pattern may be learned by repetitively using various piecesof data extracted from the population, and the blood pressure values fora new input may be estimated by calling a learned hidden layer matrix.FIG. 16 illustrates the example of the ANN algorithm configured to forma hidden layer matrix structure through machine learning to output y1(SBP) and y2 (DBP) in an output layer by corresponding extracted variouspieces of feature point data x1, x2, . . . , xN that are input in aninput layer to hidden layers, for example, Hidden Layer 1 and HiddenLayer 2. When the ANN algorithm is configured as shown in FIG. 16,output data may be estimated by using a learned hidden layer matrix withrespect to arbitrarily input data.

FIG. 17 is a diagram illustrating a linear regression analysis algorithmaccording to an exemplary embodiment.

When the linear regression analysis algorithm is applied as shown inFIG. 17, coefficient values of a linear relation equation betweenextracted feature point data and blood pressure values are calculated byanalyzing the linear relation equation using a plurality of pieces ofdata. The calculated coefficient values may be used to calculate alinear relation equation, and a blood pressure value with respect to anew input feature may be estimated by using the linear relationequation. FIG. 17 illustrates a result of estimating SBP and DBP valueswith a diastolic time t2 of a pulse waveform as a horizontal axis. Theextracted parameters may be used to calculate the linear relationequation between the parameters and the blood pressure. For example, alinear relation equation of the SBP may be obtained as Sys BP=a1t2+b1,and a linear relation equation of the DBP may be obtained as DiaBP=a2t2+b2. In this regard, a1, b1, a2, and b2 are calculated fittingparameters. As another example, a linear relation equation of the bloodpressure may be obtained as BP=a1t1+a2t2+a3t3 . . . +C. The linearrelation equation may be used to estimate a blood pressure value withrespect to a new input.

A case where the ANN algorithm is applied as a blood pressure estimationalgorithm applied to the blood pressure measuring apparatus 10 accordingto an exemplary embodiment will be described below.

When the ANN algorithm is applied as the blood pressure estimationalgorithm, a data learning process is firstly performed. During the datalearning process, a hidden layer matrix is calculated by applyingfeature points extracted with respect to a bio signal to the ANNalgorithm and then is stored in the memory 400. Thereafter, when a bloodpressure is actually measured, the feature points with respect to a biosignal are extracted, and blood pressure values, for example, a SBP, aDBP, and a HR, are calculated by using a combination of data of thefeature points and the hidden layer matrix stored in the memory 400.

FIG. 18 is a diagram illustrating a data learning process of an ANNalgorithm used by the blood pressure measuring apparatus 10 according toan exemplary embodiment.

Referring to FIG. 18, light is radiated to an examined body part, and abio signal sequence obtained by detecting a signal change in light dueto the examined body part is input (operation P1100). An input biosignal is data obtained for a predetermined period of time, for example,for 4 seconds, and from which a baseline is corrected and high frequencyHF noise is removed through processing the bio signal by using, forexample, a smoothing function or a filter. An approximate HR estimationvalue may be determined by performing fast Fourier transform (FFT) onthe bio signal sequence (operation P1200), and a maximum point of eachcardiac cycle waveform and a minimum point adjacent to the maximum pointare analyzed (operations P1300 and P1400). Thereafter, one cardiac cyclewaveform is extracted from the bio signal sequence (operation P1500),and a plurality of feature points for the cardiac cycle waveform areextracted at equidistant intervals or by using a user-defined method(operation S1600). The extracted feature points are used to extract ahidden layer matrix and blood pressure values corresponding to thehidden layer matrix (operation S1700), and a result of a learned hiddenlayer matrix is stored in the memory 400 (operation P1800).

FIG. 19 is a diagram illustrating a process of calculating bloodpressure values by using a stored hidden layer matrix after extractingfeature points via an ANN algorithm according to an exemplaryembodiment.

Referring to FIG. 19, the process of estimating and calculating bloodpressure (BP) values may be largely divided into a process (firstoperation) of extracting feature points from a bio signal and a process(second operation) of calculating the blood pressure values as a productbetween data of the extracted feature points and a stored hidden layermatrix obtained through a data learning process, for example, of the ANNalgorithm.

To extract the data of feature points from the bio signal, light isradiated towards an examined body part, and a bio signal sequenceobtained by detecting a signal change in light due to the examined bodypart is input (operation P2000). An input bio signal is data obtainedfor a predetermined period of time, for example, for 4 seconds, and fromwhich a baseline is corrected and high frequency HF noise is removed byprocessing the bio signal using, for example, a smoothing function or afilter. An approximate HR estimation value is determined by performingFFT on the bio signal sequence (operation P2100), and a maximum point ofeach cardiac cycle waveform and a minimum point adjacent to the maximumpoint is analyzed (operations P2200 and P2300). Thereafter, one cardiaccycle waveform is extracted from the bio signal sequence (operationP2400), and a plurality of feature points are extracted for the cardiaccycle waveform at equidistant intervals or by using a user-definedmethod, for example, extracting the plurality of feature points atnon-equidistant intervals (operation S2500).

The blood pressure values may be calculated by using data of theextracted feature points and a hidden layer matrix stored in the memory400. The blood pressure values may be obtained from, for example, as aproduct between the hidden layer matrix and a vector formed as the dataof the feature points. In this regard, the blood pressure valuesobtained as a result of calculation may include a SBP, a DBP, and a HR.The measured blood pressure and the HR may be displayed on the display330 and/or may be output to an external device.

FIGS. 20A through 20C are diagrams illustrating a process of collectingand predicting data that may be used during a data learning process ofFIG. 18 according to exemplary embodiments.

For example, as shown in FIG. 20A, a hidden layer matrix may be obtainedby collecting and learning data by utilizing a randomly extractedpopulation, and blood pressure values of a subject that is not includedin the population may be estimated. As another example, as shown in FIG.20B, to more accurately estimate blood pressure values, a hidden layermatrix may be obtained by classifying populations according to bodyfeatures, and collecting and learning data according to the classifiedpopulations. The blood pressure values may be estimated by using dataaccording to the classified populations corresponding to body featuresof subjects that are not included in these populations. As anotherexample, as shown in FIG. 20C, a hidden layer matrix may be obtained aplurality of number of times by collecting and learning data accordingto individuals according to circumstances when individuals take a rest,work out, or are sick during 24 hours of a day. The hidden layer matrixmay be used to estimate the blood pressure values according toindividuals.

As described with reference to FIG. 7, when a baseline of the collecteddata by using the blood pressure estimation algorithm above moves, thebaseline is corrected by using a primary, secondary, or tertiaryfunction, and when high frequency HF noise is present, the data isprocessed using, for example, a smoothing function, to remove the noise.Thereafter, one cycle is subdivided to determine minimum and maximumvalues for each cycle and extract feature points for each cycle. In thisregard, equidistant intervals or a user-defined method is used toextract the feature points. Data of the extracted feature points may beapplied to the blood pressure estimation algorithm to calculate andestimate the blood pressure values and a HR value.

As described above, according to the above exemplary embodiments, theblood pressure may be measured based on an optical signal, and thus theapparatus for and method of measuring the blood pressure may beimplemented via a cuffless structure. The blood pressure values may beestimated and calculated by using feature point data of a bio signal ina blood pressure estimation algorithm, and thus, the blood pressure maybe continuously monitored for a long time and the apparatus may beimplemented in a wearable device or portable device.

In addition, the exemplary embodiments may also be implemented throughcomputer-readable code and/or instructions on a medium, e.g., anon-transitory computer-readable medium, to control at least oneprocessing element to implement any above-described embodiments. Themedium may correspond to any medium or media which may serve as astorage and/or perform transmission of the computer-readable code.

The computer-readable code may be recorded and/or transferred on amedium in a variety of ways, and examples of the medium includerecording media, such as magnetic storage media (e.g., ROM, floppydisks, hard disks, etc.) and optical recording media (e.g., compact discread only memories (CD-ROMs) or digital versatile discs (DVDs)), andtransmission media such as Internet transmission media. Thus, the mediummay have a structure suitable for storing or carrying a signal orinformation, such as a device carrying a bitstream according to one ormore exemplary embodiments. The medium may also be on a distributednetwork, so that the computer-readable code is stored and/or transferredon the medium and executed in a distributed fashion. Furthermore, theprocessing element may include a processor or a computer processor, andthe processing element may be distributed and/or included in a singledevice.

The foregoing exemplary embodiments and advantages are merely exemplaryand are not to be construed as limiting. The present teaching can bereadily applied to other types of apparatuses. Also, the description ofthe exemplary embodiments is intended to be illustrative, and not tolimit the scope of the claims, and many alternatives, modifications, andvariations will be apparent to those skilled in the art.

What is claimed is:
 1. An apparatus configured to measure bloodpressure, the apparatus comprising: a sensor configured to radiate lightto a body part, and detect a light signal that is changed due to thebody part to which the light is radiated; a signal processor configuredto determine a bio signal, based on the light signal; and a centralprocessing unit configured to: correct the bio signal; extract featurepoints from the bio signal that is corrected; input the feature pointsinto an artificial neural network algorithm to learn a hidden layermatrix of the artificial neural network algorithm; estimate a pluralityof blood pressures as a product between the hidden layer matrix and avector that is formed from the feature points; extract one cycle of thebio signal that is corrected; extract a systolic upstroke time intervaland a diastolic time interval from the one cycle; determine a linearrelation equation of a blood pressure, from the plurality of bloodpressures over the systolic upstroke time interval and the diastolictime interval; and determine the blood pressure, based on the linearrelation equation.
 2. The apparatus of claim 1, wherein the signalprocessor is further configured to: extract a cycle of the light signal;and sample data from the cycle of the light signal at equidistant timeintervals or based on a user input.
 3. The apparatus of claim 1, whereinthe signal processor is further configured to: compare power spectrumswithin a frequency range of bio signals that are determined based onchannels; and select a channel having a maximum power spectrum from thechannels.
 4. The apparatus of claim 3, wherein, the signal processor isfurther configured to, in response to the signal processor selecting thechannel having the maximum power spectrum or using a single channel,select a part of a bio signal that corresponds to the channel that isselected or the single channel, in which a power spectrum value withinthe frequency range is greater than a value, as a valid part of the biosignal.
 5. The apparatus of claim 1, further comprising a displayconfigured to display the blood pressure.
 6. The apparatus of claim 1,further comprising a memory configured to store a blood pressureestimation algorithm and information of the bio signal.
 7. The apparatusof claim 1, wherein the sensor comprises a light emitter configured toradiate the light to the body part, and a light receiver configured todetect the light signal that is changed due to the body part, the lightreceiver comprises a photodiode or an image sensor, and the lightemitter comprises a laser diode or a light emitting diode.
 8. Theapparatus of claim 1, wherein the sensor comprises a light emitterconfigured to radiate the light to the body part, and a light receiverconfigured to detect the light signal that is changed due to the bodypart, the light emitter comprises a laser diode, and the centralprocessing unit is further configured to determine the blood pressure,based on the bio signal in response to the sensor being spaced apartfrom a skin of an examinee.
 9. The apparatus of claim 1, wherein the biosignal is periodically obtained at predetermined time intervals.
 10. Theapparatus of claim 1, wherein the central processing unit is furtherconfigured to correct the blood pressure, based on another bloodpressure that is determined by another device.
 11. The apparatus ofclaim 1, further comprising a body information interface configured toreceive body information of at least one among an age, a gender, aweight, and a height of an examinee, wherein the central processing unitis further configured to determine the blood pressure, based on the biosignal and the body information.
 12. The apparatus of claim 1, whereinthe apparatus is portable, and is implemented in one among a wristwatch, a mobile smart phone, a tablet computer, an earphone, a headset,and glasses.
 13. The apparatus of claim 1, wherein the apparatus isimplemented in a wrist watch, and the sensor is positioned on a back ofa main body or a strap of the wrist watch.
 14. A method of measuringblood pressure, the method comprising: radiating light to a body part;detecting a light signal that is changed due to the body part to whichthe light is radiated; determining a bio signal, based on the lightsignal; correcting the bio signal; extracting feature points from thebio signal that is corrected; inputting the feature points into anartificial neural network algorithm to learn a hidden layer matrix ofthe artificial neural network algorithm; estimating a plurality of bloodpressures as a product between the hidden layer matrix and a vector thatis formed from the feature points; extracting one cycle of the biosignal that is corrected; extracting a systolic upstroke time intervaland a diastolic time interval from the one cycle; determining a linearrelation equation of the blood pressure, from the plurality of bloodpressures over the systolic upstroke time interval and a diastolic timeinterval; and determining the blood pressure, based on the linearrelation equation.
 15. The method of claim 14, wherein the extractingcomprises: determining a maximum point of the bio signal that iscorrected and a minimum point adjacent to the maximum point; andextracting the feature points from the bio signal that is corrected atequidistant time intervals or based on a user input.
 16. The method ofclaim 14, wherein the correcting comprises: correcting a baseline of asequence of the bio signal; and removing high frequency noise from thesequence that is corrected.